Method

MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection [MonoTAKD V2]
[Anonymous Submission]

Submitted on 10 Mar. 2024 06:54 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We propose a framework to break down the learning
objective by integrating intra-modal distillation
with cross-modal residual distillation. In
particular, we employ a strong camera-based
teaching assistant model to distill powerful visual
knowledge effectively through intra-modal
distillation. Subsequently, we introduce the cross-
modal residual distillation to transfer the 3D
spatial cues.
Parameters:
alpha=0.2, k=5
Latex Bibtex:

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 93.84 % 83.31 % 77.95 %
Car (Orientation) 93.72 % 82.72 % 77.10 %
Car (3D Detection) 29.86 % 21.26 % 18.27 %
Car (Bird's Eye View) 43.83 % 32.31 % 28.48 %
Pedestrian (Detection) 54.33 % 38.60 % 34.12 %
Pedestrian (Orientation) 35.52 % 24.37 % 21.37 %
Pedestrian (3D Detection) 16.09 % 10.27 % 8.76 %
Pedestrian (Bird's Eye View) 19.67 % 13.47 % 11.75 %
Cyclist (Detection) 58.63 % 38.84 % 33.99 %
Cyclist (Orientation) 37.61 % 24.18 % 21.44 %
Cyclist (3D Detection) 14.59 % 8.78 % 7.50 %
Cyclist (Bird's Eye View) 19.68 % 11.66 % 10.33 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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